Multidisciplinary Design Research Articles

Multi-objective optimization of the appendages of a sailing yacht using the normal boundary intersection method

In this paper, a multidisciplinary design optimization algorithm, the Normal Boundary Intersection (NBI) method, is applied to the design of some devices of a sailing yacht. The full Pareto front is identified for two different design problems, and the optimal configurations are compared with standard devices. The great efficiency of the optimization algorithm is demonstrated by the wideness and density of the identified Pareto front.

The UK consensus supporting effective introduction of novel treatments for multiple myeloma in the National Health Service

AbstractIntroductionMultiple myeloma (MM) is a relapsing, debilitating blood cancer which remains incurable despite advances in treatments. Patients typically receive multiple lines of treatment, to which they become refractory, thereby limiting treatment options. B‐cell maturation antigen (BCMA) bispecific antibodies (BsAbs) represent a novel modality of treatment that has significant efficacy for relapsed or refractory patients.The objective was to develop consensus statements for the effective implementation of BCMA BsAbs for relapsed or refractory MM patients within the National Health Service (NHS).MethodsThe process employed a modified Delphi methodology. In March 2023, a literature review on the topic of novel treatments for MM was performed using the PubMed database.The process employed a modified Delphi methodology. Following a literature review, a steering group of eight expert clinicians identified and agreed on five main topics of focus and 44 statements. These were then developed into an online survey which was distributed to healthcare professionals working in Levels 1, 2 and 3 haematology centres in the United Kingdom. Results were then shared with the expert panel to determine conclusions. The threshold for consensus agreement was set at 75%.ResultsA total of 60 responses were received from all three centre levels. There was representation from all targeted centres. Consensus was achieved in 42 statements (95%) across three broad areas: the patient profile, initiation and step‐up dosing, monitoring and ongoing care, the role of multidisciplinary team and service designs for optimal management, consensus was not achieved for two statements. Given the level of agreement and that the stopping criteria were met, it was decided not to undertake further Delphi rounds.ConclusionThis consensus provides a framework to support the effective introduction of novel treatments for MM in the NHS. The results were used to inform a checklist for use within haematology services when considering the provision of MM care specific to BCMA BsAbs.

Embracing Levin’s Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design Through STS Approaches

AbstractThis paper investigates the synergy between Levin’s theories on technology transfer as a socio-technical learning and developmental process (TLD process), and what we learnt about socio-technical systems (STS) theories in a case study developing human robot solutions for the construction sector. Levin’s extensive work highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning and highlights the importance of incorporating cultural knowledge and skills into the technological transfer process. Contemporary STS views developed through our own work are introduced to complement and extend Levin’s theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs. To illustrate the synergies and potential challenges from Levin’s theories of technology transfer with contemporary STS concepts, we use a qualitative study of a unique case about the design and development of human-robot teams (HRTs) for construction tasks. Our findings reveal that while Levin’s theories provide a valuable foundation for understanding technology transfer and organizational change, contemporary socio-technical systems face unique challenges in the context of AI-driven human-robot teams, where intelligent robots also contribute to the socio-technical learning process. Moreover, the rapidly evolving nature of technology and innovations could exponentially impact on multidisciplinary design teams, stakeholder participation and inter-organizational dynamics. The discussions suggest an extension of co-generative learning to incorporate ‘collaborative intelligence’ between human-robot teams enabled by artificial intelligence (AI). Consequently, we suggest that Levin’s theories of technology transfer, developed before the rapid application of AI, may not have fully considered further social challenges caused by the introduction of autonomous systems such as AI-driven HRT systems. We extend Levin’s important work by suggesting that addressing such challenges requires ongoing dialogue and collaboration among researchers, practitioners, and policymakers with different disciplinary backgrounds to develop robust and reliable socio-technical systems frameworks to navigate the complexities of robotics and AI in today’s rapidly evolving technological landscape.

Mission-Based Design and Retrofit for Energy/Propulsion Systems of Solar-Powered UAVs: Integrating Propeller Slipstream Effects

Over twenty Solar-Powered Unmanned Aerial Vehicle (SPUAV) designs exist worldwide, yet few have successfully achieved uninterrupted high-altitude flight. This shortfall is attributed to several factors that cause the actual performance of SPUAV to fall short of expectations. Existing studies identify the propeller slipstream as one of these adverse factors, which leads to a decrease in the lift–drag ratio and an increase in energy consumption. However, traditional design methods for SPUAVs tend to ignore the potential adverse effects of slipstream at the top-level design phase. We find that this oversight results in a reduction in the feasible mission region of SPUAVs from 109 days to only 46 days. To address this issue, this paper presents a high-fidelity multidisciplinary design framework for the energy/propulsion systems of SPUAVs that integrates the effects of a propeller slipstream. Specifically, deep neural networks are employed to predict the lift–drag characteristics of SPUAVs under various slipstream conditions, and the energy performance is further analyzed by evaluating the time-varying state parameters throughout a day. Subsequently, the optimal solutions for the energy/propulsion systems specific to certain latitudes and dates are obtained through optimization design. The effectiveness of the proposed design framework was demonstrated on a 30-m wingspan SPUAV. The results indicated that, compared to the traditional design method, the proposed approach led to designs that more effectively accomplished closed-loop flight in designated regions and prevented the reduction of the feasible mission region. Additionally, through the targeted retrofit of the energy/propulsion systems, SPUAVs exhibited enhanced adaptability to the solar radiation characteristics of different mission points, resulting in a further expansion of the feasible mission region. Furthermore, this research also explored the variation trends in optimal solutions across different latitudes and dates and investigated the reasons and physical mechanisms behind these variations.

Multi-fidelity data mining-based design optimization framework for scramjet-based aircraft

Aircraft design faces complex multidisciplinary problems and often involves numerous design variables, making direct optimization a time-consuming task. With the development of big data and machine learning technologies, using data mining methods to assist optimization has become key to addressing the inefficiency in multidisciplinary design optimization (MDO). To address the high cost of data acquisition in current data mining, this paper proposes an aircraft design optimization framework based on multi-fidelity data mining, using a large amount of inexpensive cursory estimation data to execute the MDO process and form a multidisciplinary optimization database. Then, data mining methods are used to decouple disciplinary relationships, transforming multidisciplinary coupling problems into simpler multi-objective problems. Subsequently, under viscous CFD computation conditions, the EDGE-p-BFFD deformation method is used to fine-tune the obtained configurations, further enhancing multidisciplinary performance. Before both optimizations, data mining methods were used to analyze the impact of design variables on objective functions, forming design knowledge and completing variable selection. In the multidisciplinary optimization practice of airbreathing aircraft, it can be found that the proposed framework effectively achieves disciplinary decoupling and explores the relationship between shape and performance. In the multidisciplinary optimization of the waverider based on cursory estimations, particle swarm optimization increased the aircraft's cruising range by 8.74 %. In the multi-objective optimization of the lifting body based on viscous CFD calculations, data mining was used to transform the problem into several key objectives. The MOEA/D algorithm increased the range by 2.9 %, validating the effectiveness of the data mining-based MDO framework proposed in this paper.

Multidisciplinary Design Optimization of Alignment and Whirling Vibration Characteristics of a Submarine Propulsion Shafting Using Kriging Surrogate Model

To improve the performance indexes, such as safety, reliability and acoustic stealth, of a submarine, it is significant to optimize the dynamic characteristics of its propulsion shafting. The alignment state of a shafting has a coupling effect on its whirling vibration characteristics, and the multidisciplinary design optimization (MDO) theory can fully consider the coupling relationships between different disciplines like this, which is a scientific and effective method to achieve the design optimization of shafting dynamics. However, the iterative calculation of high-precision numerical models greatly restricts the optimization efficiency of this method. Aiming at this problem, in this paper, an MDO model was established based on the coupling dynamic analysis of submarine propulsion shafting, and the Kriging surrogate model was used to predict the state variables within each subdiscipline. Along with the reduction of computational expense, the MDO of the alignment and whirling vibration characteristics of the shafting was achieved. The studied results can be applied to the design process of submarine propulsion shafting, which can provide technical and theoretical support for improving the optimization efficiency of its coupling dynamics.

Lifetime engineering principles in highway asset management

Introduction. An important tendency of our time is sustainability, good value for money and long-term planning. These are also the goals of the recently developed discipline, lifetime engineering. Although the principles of the science were originally developed for buildings and engineering structures, they can also be adapted to public roads. The article — through a case study in Hungary — presents the applicability of life engineering science to public road asset management. Besides, examples of pavement structure design, durability, maintenance-operation and environmental protection are also presented. Problem Statement. The typical lifetime engineering science principles used in the presentation were: increasing (pavement) life cycle; complex, multi-disciplinary design methodology; modular design; effective, quality insurance methods; high level satisfaction of the customers’ needs; minimisation of lifetime costs; sustainable, end-of-life strategies. It is emphasized that lifetime engineering is based on specific basic principles that effectively promote an up-to-date approach to any discipline related to engineering infrastructure. The paper shows that the efficacy of a road asset management. can be considerably increased if some elements of lifetime engineering are utilized.

SHARE Corona Surveys: study profile

At the onset of the COVID-19 pandemic, the Survey of Health, Ageing, and Retirement in Europe (SHARE) was in a unique position to respond to the need for high quality survey data on people’s changing living situations. Implemented as two telephone interviews in the summer of 2020 and 2021 in 27 European countries and Israel, the SHARE Corona Surveys present a great advantage by their integration into the longitudinal, multidisciplinary and ex-ante harmonised design of the SHARE study. This allows researchers to trace changes from the pre-pandemic period, through the different stages of the pandemic, and the post-pandemic situation. This article lays out the research aims and how the two Corona Surveys fit in the general design of SHARE. It presents the main design features of the SHARE Corona Surveys following the survey life cycle. It starts with information on procurement, contracting, funding, ethics, and data protection and sampling, followed by information on instrument design, translations, questionnaire content and interviewer training. Last, fieldwork, panel care and data processing are described. Focused on topics of health behaviour, health care, economics and social relationships, the balanced panel sample of the two SHARE Corona Surveys comprises more than 48,000 interviews and provides valuable information on how the 50+ population coped with the COVID-19 pandemic. The experience of implementing the SHARE Corona Surveys also offers insights into use of agile project management methods for large survey infrastructures and moving towards a multi-mode design in an ongoing panel data collection project.

Design and prototyping of smart mobile grinding bots as an educational experience

Abrasive finishing of large areas is often done manually and can be time-consuming, ergonomically challenging, and even detrimental to the operator’s health. Inspired by ant robots, this paper presents a senior design project that developed a grinding robot and a quality robot, which can grind and assess a sheet metal surface. This concept of mobile grinding robots is novel and demands a multidisciplinary design approach. The main requirements included applying a controlled pressure with the grinding spindle to the surface, having a range of mobility within a predefined work area, and communication between the robots. A first prototype was delivered based on Linkbot modules, a commercial power tool as grinding spindle, sensors to measure spindle height, taped work area boundary, and surface finish. The mechanical and electronical system performance was analyzed and tested. This paper presents a proof of concept for smart mobile grinding bots and a reproducible educational experience.

Exploring residents’ expectations and designers’ perspectives towards post-earthquake permanent housing: A case study in Antakya

Post-disaster permanent housing is the backbone of the recovery efforts after destructive events. It holds great potential to adopt new technological innovations and to provide improved conditions for these everyday spaces. However, previous investigations suggest that these housing projects lack consideration of the inhabitants' expectations, needs, and values due to environmental, cultural, and social factors, resulting in a dissatisfactory built environment. Following the Kahramanmaraş earthquake on February 6, 2023, this research aims to explore the expectations of affected residents in the historic district of Antakya. We expect to contribute to decision-makers, designers, and the technology industry to develop more user-centered housing recovery efforts in the aftermath of these devastating events. The findings revealed four implications from the expectations for post-earthquake homes: (1) designing for togetherness, (2) valuing community efforts and traditional work, (3) including productional activities, and lastly, (4) preserving and reminiscing heritage values. Additionally, we discussed these four implications with multi-disciplinary designers to expand the discussion space for designing post-earthquake permanent homes in Antakya. Overall, these implications suggest some future architectural changes in homes as their residents' expectations, including the creation of new heritage and production spaces using digital, physical, and mixed reality technologies, as well as the emergence of new social gathering spaces. By conducting a case study in Antakya, our research highlights the potential for the further development of post-disaster permanent homes that not only provide shelter and safety but also enhance the overall quality of life for the residents, both for Antakya and the other relevant cases worldwide.

Volumetry as a Criterion for Suboccipital Craniectomy after Cerebellar Infarction

Objective: The aim of this study was to investigate the use of image-guided volumetry in cerebellar infarction during the decision-making process for surgery. Particular emphasis was placed on the ratio of the infarction volume to the cerebellar volume or cranial posterior fossa volume. Methods: A retrospective, multicenter, multidisciplinary study design was selected. Statistical methods such as regression analysis and ROC analysis included the volumetric data of the infarction, the posterior fossa and the cerebellum itself as new factors. Results: Thirty-eight patients (mean age 75 (SD: 13.93) years, 16 (42%) female patients) were included. The mean infarction volume was 37.79 (SD: 25.24) cm3. Patients treated surgically had a 2.05-fold larger infarction than those managed without surgery (p ≤ 0.001). Medical and surgical treatment revealed a significant difference in the ratio of the cranial posterior fossa volume to the infarction volume (medical 12.05, SD:9.09; surgical 5.14, SD: 5,65; p ≤ 0.001) and the ratio of the cerebellar volume to the infarction volume (medical 8.55, SD: 5.97; surgical 3.82, SD: 3.39; p ≤ 0.001). Subsequent multivariate regression analysis for surgical therapy showed significant results only for the posterior fossa volume to infarction volume ratio ≤/> 4:1 (OR: 1.162, CI: 1.007–1.341, p = 0.04). Younger (≤60 years) patients also had a significantly better outcome at discharge (p ≤ 0.017). A cut-off value for the infarction volume of 31.35 cm3 (sensitivity = 0.875, specificity = 0.2) was determined for the necessity of surgery. Conclusions: Volumetric data on the infarction, the posterior fossa and the cerebellum itself could be meaningful in decision-making towards surgery.

Multidisciplinary design optimization of an autonomous underwater vehicles based on random uncertainty

Herein, discipline decomposition was performed for an autonomous underwater vehicle (AUV) based on multidisciplinary design optimization (MDO), and its design parameters were determined. Parameterized analysis of the hull-form and structure disciplines was performed, and an approximate model of these disciplines was established with a high fitting accuracy. An analysis model of propulsion, energy, and system disciplines was developed based on the formula method. Based on collaborative optimization as well as discipline and system level models, a deterministic MDO framework for AUVs was established. Subsequently, an optimized solution was obtained. By considering the random uncertainty of design variables, an MDO framework for the uncertainty of AUVs was established and optimized solutions were obtained. The distribution of solutions obtained from uncertainty optimization was more concentrated and the distribution range was smaller than that obtained using deterministic optimization. Robustness analysis was performed on the initial scheme, typical deterministic optimization schemes, and typical uncertainty optimization schemes. Results showed that fluctuations in design variables may lead to constraints that exceed boundary conditions in the deterministic optimization design scheme. Using uncertainty and objective function optimization, the robustness of the overall scheme of AUVs was improved.

Preliminary Techno-Economic Study of Optimized Floating Offshore Wind Turbine Substructure

Floating offshore wind turbines (FOWTs) are still in the pre-commercial stage and, although different concepts of FOWTs are being developed, cost is a main barrier to commercializing the FOWT system. This article aims to use a shape parameterization technique within a multidisciplinary design analysis and optimization framework to alter the shape of the FOWT platform with the objective of reducing cost. This cost reduction is then implemented in 30 MW and 60 MW floating offshore wind farms (FOWFs) designed based on the static pitch angle constraints (5 degrees, 7 degrees and 10 degrees) used within the optimization framework to estimate the reduction in the levelized cost of energy (LCOE) in comparison to a FOWT platform without any shape alteration–OC3 spar platform design. Key findings in this work show that an optimal shape alteration of the platform design that satisfies the design requirements, objectives and constraints set within the optimization framework contributes to significantly reducing the CAPEX cost and the LCOE in the floating wind farms considered. This is due to the reduction in the required platform mass for hydrostatic stability when the static pitch angle is increased. The FOWF designed with a 10 degree static pitch angle constraint provided the lowest LCOE value, while the FOWF designed with a 5 degree static pitch angle constraint provided the largest LCOE value, barring the FOWT designed with the OC3 dimension, which is considered to have no inclination.

Preface: Integrating computational intelligence with multiscale analysis and multidisciplinary design approaches

Preface: Integrating computational intelligence with multiscale analysis and multidisciplinary design approaches

Multi-fidelity multidisciplinary meta-model based optimization of a slender body with fins

Multidisciplinary design optimization (MDO) involving aero-elastic simulations still proves challenging for computational cost. This paper proposes a competitive cost-effective multi-fidelity MDO strategy based on two ideas. Firstly, a new multi-fidelity fluid-structure interaction model (MF-FSI) is proposed, allowing a considerable saving of the FSI simulation cost. Secondly, the optimization cost is further reduced using a meta-model approximation of the MF-FSI computations during optimization. Therefore, the MF-FSI model is validated on an experimental case of supersonic projectile fins. Then, it is combined with a meta-model-based optimization strategy (MBO) to improve the fins design. The constrained multi-objective problem aiming to maximize the lift-to-drag ratio and minimize the fin mass is solved using both high-fidelity (HFMDO) and multi-fidelity (MFMDO). The results showed that the proposed MFMDO strategy could produce the same optimal solutions as the HFMDO one with a 52% lower cost.

A surrogate-assisted expensive constrained multi-objective global optimization algorithm and application

Expensive multi-objective optimization problems (MOPs) have seen the successful applications of surrogate-assisted evolutionary algorithms (SAEAs). Nevertheless, the majority of SAEAs are developed for costly unconstrained optimization, and costly constrained MOPs (CMOPs) have received less attention. Therefore, this article proposes a surrogate-assisted global optimization algorithm (named CTEA) for solving CMOPs within a very limited number of fitness evaluations. The proposed algorithm combines two selection frameworks, a bi-level selection framework, and an adaptive sampling framework, to enhance optimization performance. Leveraging on a constraint-improving strategy and a Pareto-based three-indicator criterion (convergence, constraint, and diversity indicators) at the different levels, the proposed bi-level selection framework can select more promising solutions. Moreover, an adaptive sampling framework is developed to prioritize objective and constraint functions and select the candidate solutions for real function evaluations according to the priority. Experimental results demonstrate that the proposed CTEA exhibits superior performance when compared with five state-of-the-art algorithms, achieving the best results in 61.9 % out of the 64 test instances. Finally, CTEA is applied to the multidisciplinary design optimization of blended-wing-body underwater gliders, and an impressive solution set is obtained.

Multidisciplinary optimization methodology for truss-braced wing aircraft using high-fidelity structure sizing

In this research, a method is developed to optimize the truss-braced wing aircraft configuration in a multidisciplinary design framework. Physics-based high-fidelity methods, that can capture the nature of the configuration changes, are employed for the disciplines where the existing classical methods are not reliable. High-fidelity geometry modeling, structure loading, structure optimization, and aeroelastic sizing methods are integrated into the aircraft multidisciplinary design and optimization. The developed algorithm is applied for the multi-objective optimization of a regional jet aircraft to minimize the cost and weight. The results demonstrate that the cost-optimum solution converges to a higher aspect ratio wing equipped with a higher bypass ratio engine, and a 7.94% reduction in the direct operating cost can be achieved. On the other hand, the weight-optimum wing planform tends to a slightly lower aspect ratio wing with a lower bypass ratio engine, while a 6.18% reduction in take-off weight is achieved. In addition to that, the findings of this study highlight the considerable effect that the engine technology has on the optimum layout, which suggests that the engine technology and its performance should also be a part of the design optimization process. The developed modular framework offers further optimization potential for the truss-braced wing aircraft, as more detailed models can be integrated.

Multidisciplinary Design and Optimization of High-Speed Hybrid Excitation Starter Generator for Aircraft Power System

Multidisciplinary Design and Optimization of High-Speed Hybrid Excitation Starter Generator for Aircraft Power System

A mobile serious game about diabetes self-management: Design and evaluation

Type 2 Diabetes Mellitus (T2DM) is a chronic condition that requires ongoing self-management and education. In recent years, there has been a growing interest in utilizing mobile serious games as a tool for patient education and engagement. This article presents the development of DiaPo, a mobile serious game designed to improve self-management education for patients with T2DM. DiaPo integrates gamification techniques to increase patient engagement and motivation while providing essential information about disease management. The development of DiaPo followed a structured design process, utilizing the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) educational system. This systematic approach allowed for the integration of best practices in educational game design and diabetes care. The development team consisted of experts in medical informatics, game design, and diabetes care, ensuring a multidisciplinary approach to the game's creation. The game's narrative focuses on a T2DM patient who earns positive points for making healthy lifestyle choices and negative points for poor ones. This gamified approach aims to reinforce positive behaviors and provide immediate feedback on negative ones. Interactive animations confirm or deny options selected by the player, further enhancing the learning experience. DiaPo offers a flexible and adaptable platform suitable for diverse audiences, promoting inclusiveness and accessibility in T2DM education. DiaPo represents a novel approach to self-management education for patients with T2DM, utilizing gamification techniques and a multidisciplinary design process to create an engaging and informative mobile serious game. By promoting inclusiveness and accessibility, DiaPo has the potential to empower patients with T2DM to take an active role in their disease management. As the field of mobile serious games continues to evolve, DiaPo stands as a promising tool for improving T2DM education and patient outcomes.

Multidisciplinary Reliability Design Optimization Modeling Based on SysML

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method’s effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development.